Membership analyzing method, apparatus, computer device and storage medium

ABSTRACT

Disclosed is a membership analyzing method, including: obtaining data information of all members of a target group; classifying members with same first type information into a data set based on predetermined keywords; obtaining the type of first type information and data sets on condition that all members have been successfully classified into a corresponding data set, otherwise, if there is any remaining member failed to be classified into any data set, inputting data information of the remaining member into a predetermined classification model, to obtain the type of first type information and a data set of each remaining member; determining a tree structure of data sets based on the type of the first type information, and producing a graph for the tree structure; setting a same identification for the nodes of the same data set, and displaying second type information of each member in a display area.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of Chinese Patent ApplicationNo. 202110132763.1 filed on Jan. 29, 2021, the contents of which areincorporated herein by reference in their entirety.

TECHNICAL FIELD

This application relates generally to the technical field of computers,and more particularly relates to a membership analyzing method, anapparatus, a computer device and a storage medium.

BACKGROUND

There are multiple members in a group, such as a business group or afamily group, and the members in the group may have hierarchicalrelationships. At present, the hierarchical relationships may beindicated in some manners. For example, regarding a business group, youcan view an organizational chart displayed in a webpage online, andsearch for membership information via the chart. However, theorganizational chart may be not updated in time, so it is difficult toget the latest organizational structure and obtain accurate membershipinformation.

SUMMARY

According to various embodiments disclosed in this application, amembership analyzing method, an apparatus, a computer device and astorage medium are provided.

A membership analyzing method being executed by a processor of acomputer device includes:

obtaining data information of all members of a target group from one ormore data sources of a storage device, wherein, the data informationincludes first type information and second type information of eachmember;

classifying members with same first type information into a data setbased on predetermined keywords, and storing the data set in the storagedevice;

determining whether each member has been successfully classified into adata set;

obtaining the type of the first type information and the data set ofeach member on condition that all members have been successfullyclassified into a corresponding data set, otherwise, if there is anyremaining member failed to be classified into any data set, determininga data set of the remaining member by inputting data information of theremaining member into a predetermined classification model, so as toobtain the type of the first type information and the data set of eachmember of the target group;

determining a tree structure of the data sets based on the type of thefirst type information, and producing a graph for the tree structure,wherein a node in the tree structure represents a member;

setting a same identification for the nodes of the same data set, anddisplaying the second type information of each member in a display areaof the node corresponding to the member on a display device, so as togenerate and display the tree structure with identifications and secondtype information of the members.

A membership analyzing apparatus includes:

a first obtaining module, configured to obtain data information of allmembers of a target group from one or more data sources, wherein, thedata information includes first type information and second typeinformation of each member;

an analyzing module, configured to classify members with same first typeinformation into a data set based on predetermined keywords;

a determining module, configured to determine whether each member hasbeen successfully classified into a data set;

a second obtaining module, configured to obtain the type of the firsttype information and the data set of each member on condition that allmembers have been successfully classified into a corresponding data set;

a third obtaining module, configured to determine a data set of aremaining member by inputting data information of the remaining memberinto a predetermined classification model if there is any remainingmember failed to be classified into any data set, so as to obtain thetype of the first type information and the data set of each member ofthe target group;

a producing module, configured to determine a tree structure of the datasets based on the type of the first type information, and produce agraph for the tree structure, wherein a node in the tree structurerepresents a member;

a displaying module, configured to set a same identification for thenodes of the same data set, and display the second type information ofeach member in a display area of the node corresponding to the member,so as to generate and display the tree structure with identificationsand second type information of the members.

A computer device includes a memory and one or more processors, whereinthe memory stores computer-readable program instructions, and when thecomputer-readable program instructions are executed by the one or moreprocessors, the one or more processors are enabled to perform followingsteps:

obtaining data information of all members of a target group from one ormore data sources, wherein, the data information includes first typeinformation and second type information of each member;

classifying members with same first type information into a data setbased on predetermined keywords;

determining whether each member has been successfully classified into adata set;

obtaining the type of the first type information and the data set ofeach member on condition that all members have been successfullyclassified into a corresponding data set, otherwise, if there is anyremaining member failed to be classified into any data set, determininga data set of the remaining member by inputting data information of theremaining member into a predetermined classification model, so as toobtain the type of the first type information and the data set of eachmember of the target group;

determining a tree structure of the data sets based on the type of thefirst type information, and producing a graph for the tree structure,wherein a node in the tree structure represents a member;

setting a same identification for the nodes of the same data set, anddisplaying the second type information of each member in a display areaof the node corresponding to the member, so as to generate and displaythe tree structure with identifications and second type information ofthe members.

A non-volatile computer-readable storage media that storescomputer-readable program instructions is provided, when thecomputer-readable program instructions are executed by one or moreprocessors, the one or more processors are enabled to perform followingsteps:

obtaining data information of all members of a target group from one ormore data sources, wherein, the data information includes first typeinformation and second type information of each member;

classifying members with same first type information into a data setbased on predetermined keywords;

determining whether each member has been successfully classified into adata set;

obtaining the type of the first type information and the data set ofeach member on condition that all members have been successfullyclassified into a corresponding data set, otherwise, if there is anyremaining member failed to be classified into any data set, determininga data set of the remaining member by inputting data information of theremaining member into a predetermined classification model, so as toobtain the type of the first type information and the data set of eachmember of the target group;

determining a tree structure of the data sets based on the type of thefirst type information, and producing a graph for the tree structure,wherein a node in the tree structure represents a member;

setting a same identification for the nodes of the same data set, anddisplaying the second type information of each member in a display areaof the node corresponding to the member, so as to generate and displaythe tree structure with identifications and second type information ofthe members.

Details of one or more embodiments of this application are provided inthe following accompanying drawings and descriptions. Other features andadvantages of this application become clear from the specification, theaccompanying drawings, and the claims.

BRIEF DESCRIPTION OF DRAWINGS

To describe the technical solutions in the embodiments of thisapplication more clearly, the following briefly describes theaccompanying drawings required for describing the embodiments.Apparently, the accompanying drawings in the following descriptions showmerely some embodiments of this application, and a person of ordinaryskill in the art may still derive other drawings from these accompanyingdrawings without creative efforts.

FIG. 1 is a schematic flowchart of a membership analyzing methodaccording to one or more embodiments;

FIG. 2 is a graphic of a tree structure according to the embodiments ofFIG. 1 ;

FIG. 3 is a structural diagram of a membership analysis apparatusaccording to one or more embodiments; and

FIG. 4 is an internal structural diagram of a computer device accordingto one or more embodiments.

DESCRIPTION OF EMBODIMENTS

To make the technical solutions and advantages of this applicationclearer and more comprehensible, the following further describes thisapplication in detail with reference to the accompanying drawings andembodiments. It should be understood that the specific embodimentsdescribed herein are merely intended to explain this application, andare not intended to limit this application.

In an embodiment, as shown in FIG. 1 , a membership analyzing method isprovided. The method, which is executed by a processor of a computerdevice show in FIG. 4 , includes following steps.

Step S1: obtaining data information of all members of a target groupfrom one or more data sources of a storage device, wherein, the datainformation includes first type information and second type informationof each member.

The data sources may include one or more databases which store datainformation collected from various websites, electronic newspaper, orother electronic media. A target group's members may include investors,directors, employees etc. of a company or an organization, or members ofa family. For example, the data information may include a name and anage information of a member in the target group, an ID number, a homeaddress, a face image, a work unit, a nationality, a job information ina company which the member works for, etc.

For example, for a company, the first type information is about jobinformation. The job information may include a job position or aresponsibility. The second type information is about the name, the faceimage, and the nationality, etc. For example, for a family, the firsttype information may include the age, or the name and the age, thus thehierarchical relationships of the family can be sorted out by the age,or the hierarchical relationship of the family can be sorted outcombining the name with the age. The second type information may includethe face image and the nationality, etc. In all the embodiments of thepresent invention, the target group is the company.

Step S2: classifying members with same first type information into adata set based on predetermined keywords, and storing the data set inthe storage device.

The predetermined keywords include first type keywords and second typekeywords, the first type keywords are about standard keywords, and thesecond type keywords are about non-standard keywords. The second typekeywords are customized keywords, which can be edited.

Taking the first type information as the job information as an example,the second type information of a member includes a surname and a givenname of the member, the first type keywords can be a standard name of ajob position, such as sales representatives, sales managers, salesdirectors, and the second type keywords can be a non-standard name of ajob position, such as governor. The type of the first type informationmay include director, manager, sale, etc.

In this embodiment, the members in the target group are classified intoseveral data sets based on the type of the first type information, thosemembers with the same type of the first type information are classifiedinto the same data set. For example, members with the same job positionare classified into the data set labeled as “A”.

Step S20: determining whether each member has been successfullyclassified into a data set. If each member has been successfullyclassified into a corresponding data set, the procedure goes to step S3.

Step S3: obtaining the type of the first type information and the dataset of each member on condition that all members have been successfullyclassified into a corresponding data set.

In step S20, if there is any remaining member failed to be classifiedinto any data set, the procedure goes to step S4 from step S20.

Step S4: determining a data set of the remaining member by inputtingdata information of the remaining member into a predeterminedclassification model, so as to obtain the type of the first typeinformation and the data set of each member of the target group.

Since the predetermined keywords may not be able to exhaust all the jobpositions in the actual situation, there may be members failed to beclassified into any data set. At this time, a classification model canbe used to obtain the type of the first type information and data set ofthe members.

The classification model is a model trained on massive data. Theclassification model can be a common classification models, such as aconvolution neural network (CNN) model, a recurrent neural network (RNN)model, or a support vector machine (SVM) model. The input of theclassification model is a variety of job positions, and the output ofthe classification model may include types of the job positions and thedata set corresponding to each input job position, or the output mayinclude one or more scores indicating one or more probabilities thatwhich type the input job position belongs to. In the latter case, thetype of the job position corresponding to the highest score isdetermined as the type of the job position of a member.

In this embodiment, a training process of the classification modelincludes:

obtaining various job positions, and marking the type of the jobpositions, and dividing the job positions into a sample set and a testset according to a predetermined ratio (e.g., 8:2). The sample set willbe inputted into the classification model for training, and the test setwill be inputted into the classification model for testing. An accuracyrate of the classification model will be obtained. If the accuracy rateof the classification model is greater than a predetermined value (e.g.,0.95), the training is ended.

Step S5: determining a tree structure of the data sets based on the typeof the first type information, and producing a graph for the treestructure, wherein a node in the tree structure represents a member.

In this embodiment, there may be one or more types of job positionwithin a company, different types of job position indicate the actualranks. For example, the types of job position may include director,manager, sale and so on in a company, wherein 10 members as a first dataset are sales representative, 2 members as a second data set are salesmanager, and 1 member as a third data set is sales director. The actualranks from top to bottom can be director, manager, sale, and thehierarchical relationship of the data sets from top to bottom will bedetermined by the ranks as: sales director>sales manager>salesrepresentative. A tree structure can be determine based on the ranks ofthe data sets. The tree structure will be performed graphic processingto generate a graphic, wherein a node in the tree structure represents amember. The tree structure is displayed in a user interface of acomputer device, wherein the user interface includes multiple displayareas, and one node in the tree structure occupies one display area. Thetree structure is preferably an organization chart (ochart).

Step S6: setting a same identification for the nodes of the same dataset, and display the second type information of each member in a displayarea of the node corresponding to the member on a display device, so asto generate and display the tree structure with identifications andsecond type information of the members.

The members corresponding to the nodes with the same identification canbe regarded as members of a same department. Display the second typeinformation of each member on a display area of the node correspondingto the member, so as to generate and display the tree structure withidentifications and second type information of the members, wherein thesecond type information include the name, the face image, and thenationality, etc.

In this embodiment, as shown in the FIG. 2 , there are three data sets,wherein a first data set can be directors, a second data set can bemanagers, and a third data set can be sales. Each small box in the FIG.2 represents a node of the tree structure. The members in the same dataset are identified with the same identification. For example, the nodescorresponding to the members in the same data set are marked with a samecolor, and the nodes corresponding to the members in different data setsare marked with different colors. For example, the nodes correspondingto the director's data set are marked in red, the nodes corresponding tothe manager's data set are marked in yellow, and the nodes correspondingto the sales data set are marked in green. In addition, the second typeinformation can be identified in a display area occupied by thecorresponding node of the tree structure.

As described above, this embodiment first classifies members with samefirst type information into a data set based on predetermined keywordsafter obtaining the data information from a target group, determineswhether each member has been successfully classified into a data set, ifeach member has been successfully classified into a data set, the typeof the first type information and the data set of each member can beobtained, otherwise, if there is any remaining member failed to beclassified into any data set, determines a data set of the remainingmember by inputting data information of the remaining member into apredetermined classification model, so as to obtain the type of thefirst type information and the data set, and then determines a treestructure of the data sets based on the type of the first typeinformation, a node in the tree structure represents a member, finally,sets a same identification for the nodes of the same data set, and thesecond type information of each member will be displayed on a displayarea of the user interface of the computer device where occupied by thenode. This embodiment obtains data information from one or more datasources, uses a combination of predetermined keywords and classificationmodels to perform classification on the data information, and determinesa tree structure of the data sets based on the type of the first typeinformation, this embodiment bases on technology of Big Data, it canaccurately analyze the hierarchical relationship of group members, andsave time and effort.

In a second embodiment, based on the above-mentioned embodiment of FIG.1 , the method further includes: determining members with the samesurname, generating and displaying a first control icon for the nodes ofthe members with the same surname, and dynamically displaying the nodesof the members with the same surname in response to the first controlicon is clicked.

In this embodiment, the user interface is divided into two areas,wherein a first area includes multiple display areas which occupied bythe nodes of the tree structure, and a second area includes theremaining area of the user interface. One or more first control iconsare generated, and the first control icons are displayed in apredetermined position in the user interface of the computer device, forexample, the predetermined position may be a position of the second areaof the user interface. One first control icon corresponds to two or morenodes of the members with the same surname. The nodes of the memberswith the same surname can be dynamically displayed in response to thecorresponding first control icon is clicked, for example, the nodes ofthe members with the same surname “Zhang” can be flashed in response tothe corresponding first control icon is clicked. In this way, the methodroughly analyzes whether the company is a family business, and themembership of the members of the target group will be analyzed in moredimensions, and the objectivity and accuracy of analysis will beimproved.

Furthermore, a family face recognition model can be trained to recognizewhether the face images of the members of a company are face images ofmembers in a same family. Specifically, the face images of members withthe same surname are determined, and the face images of members with thesame surname are inputted into the family face recognition model toobtain the result of whether multiple or all members with the samesurname are members of the same family. In this way, the method analyzesaccurately whether the company is a family business, and thehierarchical relationship of the members of the company can be analyzedmore accurately.

In a third embodiment, based on the above-mentioned embodiments, themethod further includes: determining members with the same surname andgiven name, generating and displaying a second control icon for thenodes of the members with the same surname and given name, andhighlighting the nodes of the members with the same surname and givenname in response to the second control icon is clicked, and merging datainformation of the members with the same surname and given name.

In this embodiment, considering that the data information of a membermay be obtained from multiple different data sources, so there may beduplicate data information from two or more different data sources. Inorder to delete duplicate member information and simplify the treestructure, one or more second control icon are generated and displayedin a predetermined position in the user interface of the computerdevice, for example, the predetermined position may be a position of thesecond area of the user interface. One second control icon correspondsto two or more nodes of members with the same surname and given name.The nodes of the members with the same surname and given name can behighlighted, and the data information (e.g., two data records) of themembers with the same surname and given name are merged as one. Forexample, the face images and the remaining data information of themembers with the same surname and given name are merged. In this way,the members who are suspected to be duplicate members can be identified,and can be manually reviewed to determine whether the members areduplicate members, furthermore, the duplicate members can be removed andthe data information of the duplicate members can be merged.

In a fourth embodiment, based on the above-mentioned embodiments, themethod further includes:

obtaining face images of the members of the target group, inputting eachof the face images into a predetermined gender analysis model, anddetermining a gender of each member corresponding to a face image inresponse that the gender analysis model successfully output an analysisresult, otherwise, determining the gender of the member corresponding tothe face image by analyzing the same surname and given name of themember, in response that the gender analysis model failed to output ananalysis result, and displaying the gender of each member on thecorresponding node of the member in the tree structure.

In this embodiment, the gender analysis model can be a classificationmodel, and the classification model can be a classification model, suchas the CNN model, the RNN model, or the SVM model. The inputs of theclassification model are various face images, and the outputs of theclassification model include a gender corresponding to each input faceimage, or the outputs include scores, which represent probabilities ofan input face image belongs to male or female. In the latter case, agender (e.g., male or female) with the highest score is determined asthe gender of the input face image.

In this embodiment, a training process of the gender analysis modelincludes:

obtaining multiple face images (e.g., 100,000) of the members, andmarking a gender of each member according to a corresponding face imageof the member;

dividing the face images into a sample set and a validation setaccording to a predetermined ratio(e.g., 8:2);

training the gender analysis model using the sample set, and validatingthe gender analysis model using the validation set, and obtaining anaccuracy rate of the gender analysis model;

ending the training until the accuracy rate is greater than or equal toa predetermined value (e.g., 0.95), thus the trained gender analysismodel is determined.

In this embodiment, in the case the gender analysis model fails torecognize the gender of any member according to the input face image, orthe scores are very similar, the gender of the member can be furtherdetermined by name analysis. Taking into account the differences betweenmale and female names in different regions, the corresponding gender canbe accurately determined. For example, in Japan and the United Kingdom,the differences between male and female names are relatively large, andthus the gender of the member can be determined based on thedifferences.

The gender is identified in the user interface of the computer device,where occupied by the node of the corresponding member after the genderof the member is determined. The gender may first be hidden in the userinterface and just displayed according to user's activation. Forexample, the detailed information of a member is displayed in responseto the node corresponding to a member is clicked, meanwhile, themember's gender is displayed. In this way, the display dimensions of thetree structure can be further increased, the membership can be analyzedin more dimensions, and the objectivity and accuracy of the analysis canbe improved.

In a fifth embodiment, based on the above-mentioned embodiments, themethod further includes:

receiving an adjustment instruction for a node corresponding to a memberin the tree structure, and optimizing the classification model byadjusting the position of the node corresponding to the member in thetree structure based on the adjustment instruction.

In this embodiment, the hierarchical relationship of memberscorresponding to nodes in the tree structure can be checked manually. Ifa hierarchy of a member in the tree structure is wrong, the hierarchy ofthe member can be adjusted, so that the hierarchical relationship of themember in the tree structure is right. For example, a node correspondingto a member in the tree structure can be selected and dragged, and canbe moved to another display area from present display area, so as toachieve a right hierarchy for the member.

The type of the first type information and data set can be determinedagain after adjusting the hierarchy of the member, and can be used asone of the sample sources of the training set of the aboveclassification model, so that the classification model can be optimized.In this way, the objectivity and accuracy can be further improved.

The present invention also provides a membership analyzing apparatus,which corresponds to the membership analyzing method in theabove-mentioned embodiments in a one-to-one correspondence. As shown inFIG. 3 , including:

The first obtaining module 101 is configured to obtain data informationof all members of a target group from one or more data sources, wherein,the data information includes first type information and second typeinformation of each member;

The analyzing module 102 is configured to classify members with samefirst type information into a data set based on predetermined keywords;

The determining module 103 is configured to determine whether eachmember has been successfully classified into a data set;

The second obtaining module 104 is configured to obtain the type of thefirst type information and the data set of each member on condition thatall members have been successfully classified into a corresponding dataset;

The third obtaining module 105 is configured to determine a data set ofa remaining member by inputting data information of the remaining memberinto a predetermined classification model if there is any remainingmember failed to be classified into any data set, so as to obtain thetype of the first type information and the data set of each member ofthe target group;

The producing module 106 is configured to determine a tree structure ofthe data sets based on the type of the first type information, andproduce a graph for the tree structure, wherein a node in the treestructure represents a member;

The displaying module 107 is configured to set a same identification forthe nodes of the same data set, and display the second type informationof each member in a display area of the node corresponding to themember, so as to generate and display the tree structure withidentifications and second type information of the members.

The specific definition of the membership analyzing apparatus can bereferred to the above definition of the membership analyzing method,which will not be repeated here. Each module in the above-mentionedmembership analyzing apparatus can be implemented in whole or in part bysoftware, hardware and a combination thereof. The above-mentionedmodules may be embedded or independent of a processor in a computerdevice in the form of hardware, or may be stored in a memory of thecomputer device in the form of software, It can also be stored in thememory of the computer device in the form of software, so that theprocessor can call and execute the operations corresponding to the abovemodules.

The present invention also provides a computer device. The computerdevice is a device that can automatically perform numerical calculationand/or information processing in accordance with pre-set or storedinstructions. The computer device may be a personal computer(PC), asmart phone, a tablet, a computer, or a single network server, a servergroup composed of multiple network servers, or a cloud composed of alarge number of hosts or network servers based on cloud computing, inwhich cloud computing is a type of distributed computing, a supervirtual computer composed of a group of loosely coupled computer sets.

As shown in FIG. 4 , the computer device may include, but is not limitedto, a memory 11, a processor 12, and a network interface 13 that can becommunicatively connected to each other through a system bus. The memory11 stores computer-readable program instructions that can run on theprocessor 12. It should be pointed out that FIG. 4 only shows a computerdevice with components 11-13, but it should be understood that it is notrequired to implement all the illustrated components, more or fewercomponents can be implemented as an alternative.

The memory 11 may be a non-volatile memory and/or volatile memory.Non-volatile memory may include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), or flash memory. Volatile memory may includerandom access memory (RAM) or external cache memory. As an illustrationand not a limitation, RAM is available in many forms, such as Static RAM(SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data RateSDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronization Link DRAM(SLDRAM), Rambus Direct RAM (RDRAM), Direct Rambus DRAM(DRDRAM), andRambus Dynamic RAM (RDRAM), etc. In this embodiment, the readablestorage medium of the memory 11 is generally used to store an operatingsystem and various application software installed in the computerdevice, for example, to store the computer-readable programinstructions. In addition, the memory 11 can also be used to temporarilystore various types of data that have been output or will be output.

The processor 12 may be a central processing unit (CPU), a controller, amicrocontroller, a microprocessor, or other data processing chip in someembodiments for running the computer-readable program instructions orprocessing data stored in the memory 11.

The network interface 13 may include a standard wireless networkinterface and a wired network interface. The network interface 13 can beusually used to establish a communication connection between thecomputer device and other electronic devices.

The computer-readable program instructions are stored in the memory 11,and the computer-readable instructions can be executed by one or moreprocessors 12, the one or more processors 12 are enabled to performfollowing steps:

obtaining data information of all members of a target group from one ormore data sources, wherein, the data information includes first typeinformation and second type information of each member;

classifying members with same first type information into a data setbased on predetermined keywords;

determining whether each member has been successfully classified into adata set;

obtaining the type of the first type information and the data set ofeach member on condition that all members have been successfullyclassified into a corresponding data set, otherwise, if there is anyremaining member failed to be classified into any data set, determininga data set of the remaining member by inputting data information of theremaining member into a predetermined classification model, so as toobtain the type of the first type information and the data set of eachmember of the target group;

determining a tree structure of the data sets based on the type of thefirst type information, and producing a graph for the tree structure,wherein a node in the tree structure represents a member;

setting a same identification for the nodes of the same data set, anddisplaying the second type information of each member in a display areaof the node corresponding to the member, so as to generate and displaythe tree structure with identifications and second type information ofthe members.

Preferably, the first type information of a member includes jobinformation of the member, and the second type information of a memberincludes a surname and a given name of the member.

Further, the processor also performs following steps: determiningmembers with the same surname, generating and displaying a first controlicon for the nodes of the members with the same surname, and dynamicallydisplaying the nodes of the members with the same surname in response tothe first control icon is clicked.

Further, the processor also performs following steps: determiningmembers with the same surname and given name, generating and displayinga second control icon for the nodes of the members with the same surnameand given name, and highlighting the nodes of the members with the samesurname and given name in response to the second control icon isclicked, and merging data information of the members with the samesurname and given name.

Further, the processor also performs following steps: obtaining faceimages of the members, inputting each of the face images into apredetermined gender analysis model, and determining a gender of eachmember corresponding to a face image in response that the genderanalysis model successfully output an analysis result, otherwise,determining the gender of the member corresponding to the face image byanalyzing the same surname and given name of the member, in responsethat the gender analysis model failed to output an analysis result, anddisplaying the gender of each member on the corresponding node of themember in the tree structure.

Preferably, a training process of the gender analysis model includes:

obtaining multiple face images of the members, and marking a gender ofeach member according to a corresponding face image of the member;

dividing the face images into a sample set and a validation setaccording to a predetermined ratio ;

training the gender analysis model using the sample set, and validatingthe gender analysis model using the validation set, and obtaining anaccuracy rate of the gender analysis model;

ending the training until the accuracy rate is greater than or equal toa predetermined value.

Further, the processor also performs following steps: determining faceimages of the members with the same surname, inputting the face imagesinto a predetermined family face recognition model, obtaining a resultoutputted by the family face recognition model, and analyzing weatherthe members with the same surname are family members based on theresult.

Further, the processor also performs following steps:

receiving an adjustment instruction for a node corresponding to a memberin the tree structure, and optimizing the classification model byadjusting the position of the node corresponding to the member in thetree structure based on the adjustment instruction.

The invention also provides a non-volatile computer-readable storagemedium, the non-volatile computer-readable storage medium may be anon-volatile memory and/or volatile memory, on which computer-readableprogram instructions are stored. When the computer-readable programinstructions are executed by a processor, the steps of the method foranalyzing the membership in the foregoing embodiments are implemented,for example, the steps from step S1 to step S6 shown in FIG. 1 , or,when the computer-readable program instructions are executed by theprocessor, the function of each module/unit of the membership analyzingapparatus in the above-mentioned embodiments is realized, for example,the function of the module 101 to the module 107 shown in FIG. 3 . Toavoid repetition, it won't repeat them here.

A person of ordinary skill in the art can understand that all or part ofthe processes in the above-mentioned embodiments can be implemented by acomputer program instructing relevant hardware. When the computerprogram is executed, it may include the processes of the above-mentionedmethod embodiments.

The order of the above embodiments does not represent the advantages anddisadvantages of the embodiments.

The technical features of the foregoing embodiments may be randomlycombined. For brevity of description, not all possible combinations ofthe technical features of the foregoing embodiments are described.However, all the combinations of these technical features should beconsidered to be within the scope of this specification provided thatthe combinations are not contradictory.

The foregoing embodiments are merely several implementations of thisapplication, and description of the implementations is relativelyspecific and detailed, but shall not be understood as a limitation onthe scope of the present invention. It should be noted that a person ofordinary skill in the art may make several variations and improvementswithout departing from the concept of this application, and thevariations and improvements shall fall within the protection scope ofthis application. Therefore, the protection scope of this applicationpatent shall be subject to the appended claims.

The invention claimed is:
 1. A membership analyzing method executed by aprocessor of a computer device, the membership analyzing methodcomprising: obtaining personal information of all persons of a targetgroup from one or more data sources of a storage device, wherein thepersonal information includes first type information and second typeinformation of each person; classifying persons with same first typeinformation into a same respective set based on predetermined keywords,and storing the set in the storage device; determining whether eachperson has been successfully classified into a set; their respectivesets, obtaining a type of the first type information and the set of eachperson, otherwise, in response to there being at least one remainingperson failing to be classified into any set, determining a set of eachof the at least one remaining person by inputting the personalinformation of each of the at least one remaining person into apredetermined classification model thus obtaining the type of the firsttype information and the set of each person of the target group;determining a tree structure of the sets based on the types of the firsttype information, and producing a graph for the tree structure, whereineach node in the tree structure represents a person; setting a sameidentifier for the nodes of each same set, and displaying the secondtype information of each person in a display area of the nodecorresponding to the person on a display device, so as to generate anddisplay the tree structure with identifiers and second type informationof the persons; wherein setting a same identifier for the nodes of eachsame set comprises marking the nodes corresponding to the personsbelonging to each same set with a same color, and marking the nodesbelonging to different sets with different colors; wherein the firsttype of information of each person includes job information of theperson, and the second type of information of each person includes asurname and a given name of the person; wherein the method furthercomprises: determining persons with the same surname, generating anddisplaying a first control icon for the nodes of the persons with thesame surname, and dynamically displaying the nodes of the persons withthe same surname in response to the first control icon being clicked,allowing a user to preliminarily analyze whether the target group is afamily business; wherein the method further comprises: determiningpersons with the same surname and given name, generating and displayinga second control icon for the nodes of the persons with the same surnameand given name, and in response to the second control icon beingclicked, highlighting the nodes of the persons with the same surname andgiven name and merging the personal information of the persons with thesame surname and given name so that duplicate persons in the treestructure are identified and removed; wherein the method furthercomprises: receiving an adjustment instruction for a node correspondingto a person in the tree structure, and optimizing the classificationmodel by adjusting a position of the node corresponding to the person inthe tree structure based on the adjustment instruction.
 2. The methodaccording to claim 1, further comprising: obtaining face images of thepersons, inputting each of the face images into a predetermined genderanalysis model, and determining a gender of each person corresponding toa face image in response to the gender analysis model successfullyoutputting an analysis result, otherwise in response to the genderanalysis model failing to output an analysis result, determining thegender of the person corresponding to the face image by analyzing thesame surname and given name of the person, and displaying the gender ofeach person on the corresponding node of the person in the treestructure.
 3. The method according to claim 2, wherein a trainingprocess of the gender analysis model comprises: marking a gender of eachperson according to a corresponding face image of the person; dividingthe face images into a sample set and a validation set according to apredetermined ratio ; training the gender analysis model using thesample set, and validating the gender analysis model using thevalidation set, and obtaining an accuracy rate of the gender analysismodel; ending the training until the accuracy rate is greater than orequal to a predetermined value.
 4. The method according to claim 1,further comprising: obtaining face images of the persons, determiningthe face images of the persons with a same surname, inputting the faceimages of the persons with the same surname into a predetermined familyface recognition model, obtaining a result outputted by the family facerecognition model, and analyzing whether the persons with the samesurname are family members based on the result.
 5. A computer device,comprising a memory and one or more processors, wherein the memorystores computer-readable program instructions, and when thecomputer-readable program instructions are executed by the one or moreprocessors, the one or more processors are enabled to perform followingsteps: obtaining personal information of all persons of a target groupfrom one or more data sources, wherein the personal information includesfirst type information and second type information of each person;classifying persons with same first type information into a samerespective set based on predetermined keywords; determining whether eachperson has been successfully classified into a set; obtaining a type ofthe first type information and the set of each person, otherwise, inresponse to there being at least one remaining person failing to beclassified into any set, determining a set of each of the at least oneremaining person by inputting the personal information of each of the atleast one remaining person into a predetermined classification modelthus obtaining the type of the first type information and the set ofeach person of the target group; determining a tree structure of thesets based on the types of the first type information, and producing agraph for the tree structure, wherein each node in the tree structurerepresents a person; setting a same identifier for each same set, anddisplaying the second type information of each person in a display areaof the node corresponding to the person, so as to generate and displaythe tree structure with identifiers and second type information of thepersons; wherein setting a same identifier for the nodes of each sameset comprises marking the nodes corresponding to the persons belongingto each same set with a same color, and marking the nodes belonging todifferent sets with different colors; wherein the first type ofinformation of each person includes job information of the person, andthe second type of information of each person includes a surname and agiven name of the person; wherein the method further comprises:determining persons with the same surname, generating and displaying afirst control icon for the nodes of the persons with the same surname,and dynamically displaying the nodes of the persons with the samesurname in response to the first control icon being clicked, allowing auser to preliminarily analyze whether the target group is a familybusiness; wherein the method further comprises: determining persons withthe same surname and given name, generating and displaying a secondcontrol icon for the nodes of the persons with the same surname andgiven name, and in response to the second control icon being clicked,highlighting the nodes of the persons with the same surname and givenname and merging the personal information of the persons with the samesurname and given name so that duplicate persons in the tree structureare identified and removed; wherein the method further comprises:receiving an adjustment instruction for a node corresponding to a personin the tree structure, and optimizing the classification model byadjusting a position of the node corresponding to the person in the treestructure based on the adjustment instruction.
 6. The computer deviceaccording to claim 5, wherein the processor further performs followingsteps: obtaining face images of the persons, inputting each of the faceimages into a predetermined gender analysis model, and determining agender of each person corresponding to a face image in response to thegender analysis model successfully outputting an analysis result,otherwise in response to the gender analysis model failing to output ananalysis result, determining the gender of the person corresponding tothe face image by analyzing the same surname and given name of theperson, and displaying the gender of each person on the correspondingnode of the person in the tree structure.
 7. The computer deviceaccording to claim 6, wherein the processor further performs followingsteps: marking a gender of each person according to a corresponding faceimage of the person ; dividing the face images into a sample set and avalidation set according to a predetermined ratio ; training the genderanalysis model using the sample set, and validating the gender analysismodel using the validation set, and obtaining an accuracy rate of thegender analysis model; ending the training until the accuracy rate isgreater than or equal to a predetermined value.
 8. The computer deviceaccording to claim 5, wherein the second type information furtherincludes avatar, and the processor further performs following steps:obtaining face images of the persons, determining the face images of thepersons with a same surname, inputting the face images of the personswith the same surname into a predetermined family face recognitionmodel, obtaining a result outputted by the family face recognitionmodel, and analyzing whether the persons with the same surname arefamily members based on the result.
 9. A non-transitory non-volatilecomputer-readable storage media, wherein the computer-readable storagemedium stores computer-readable program instructions, when thecomputer-readable program instructions are executed by one or moreprocessors, the one or more processors are enabled to perform followingsteps: obtaining personal information of all persons of a target groupfrom one or more data sources, wherein the personal information includesfirst type information and second type information of each person;classifying persons with same first type information into a samerespective set based on predetermined keywords; determining whether eachperson has been successfully classified into a set; obtaining the typeof the first type information and the set of each person, otherwise, inresponse to there being at least one remaining person failing to beclassified into any set, determining a set of each of the at least oneremaining person by inputting the personal information of each of the atleast one remaining person into a predetermined classification modelthus obtaining the type of the first type information and the set ofeach person of the target group; determining a free structure of thesets based on the types of the first type information, and producing agraph for the free structure, wherein each node in the free structurerepresents a person; setting a same identifier for each same set, anddisplaying the second type information of each person in a display areaof the node corresponding to the person, so as to generate and displaythe free structure with identifiers and second type information of thepersons; wherein setting a same identifier for the nodes of each sameset comprises marking the nodes corresponding to the persons belongingto each same set with a same color, and marking the nodes belonging todifferent sets with different colors; wherein the first type ofinformation of each person includes job information of the person, andthe second type of information of each person includes a surname and agiven name of the person; wherein the method further comprises:determining persons with the same surname, generating and displaying afirst control icon for the nodes of the persons with the same surname,and dynamically displaying the nodes of the persons with the samesurname in response to the first control icon being clicked, allowing auser to preliminarily analyze whether the target group is a familybusiness; wherein the method further comprises: determining persons withthe same surname and given name, generating and displaying a secondcontrol icon for the nodes of the persons with the same surname andgiven name, and in response to the second control icon being clicked,highlighting the nodes of the persons with the same surname and givenname and merging the personal information of the persons with the samesurname and given name so that duplicate persons in the free structureare identified and removed; wherein the method further comprises:receiving an adjustment instruction for a node corresponding to a personin the free structure, and optimizing the classification model byadjusting a position of the node corresponding to the person in the freestructure based on the adjustment instruction.